



'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 							a__( snake_case__     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   )							->							int:
					super().__init__()
					self.register_modules(transformer=_UpperCAmelCase					,   vae=_UpperCAmelCase					,   scheduler=_UpperCAmelCase						)

					# create a imagenet -> id dictionary for easier use
					snake_case__	     ={}
					if idalabel is not None:
							for key, value in idalabel.items():
									for label in value.split(','						):
											snake_case__	     =int(_UpperCAmelCase						)
							snake_case__	     =dict(sorted(self.labels.items()						)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							List[int]:

					if not isinstance(_UpperCAmelCase					,   _UpperCAmelCase						):
							snake_case__	     =list(_UpperCAmelCase						)

					for l in label:
							if l not in self.labels:
									raise ValueError(
									    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""						)

					return [self.labels[l] for l in label]






			@torch.no_grad()
			def __call__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase = 4.0					,   _UpperCAmelCase = None					,   _UpperCAmelCase = 50					,   _UpperCAmelCase = "pil"					,   _UpperCAmelCase = True					,   )							->							Union[ImagePipelineOutput, Tuple]:
					snake_case__	     =len(_UpperCAmelCase						)
					snake_case__	     =self.transformer.config.sample_size
					snake_case__	     =self.transformer.config.in_channels

					snake_case__	     =randn_tensor(
					    shape=(batch_size, latent_channels, latent_size, latent_size)					,   generator=_UpperCAmelCase					,   device=self.device					,   dtype=self.transformer.dtype					,   )
					snake_case__	     =torch.cat([latents] * 2						) if guidance_scale > 1 else latents

					snake_case__	     =torch.tensor(_UpperCAmelCase					,   device=self.device						).reshape(-1						)
					snake_case__	     =torch.tensor([1000] * batch_size					,   device=self.device						)
					snake_case__	     =torch.cat([class_labels, class_null]					,   0						) if guidance_scale > 1 else class_labels

					# set step values
					self.scheduler.set_timesteps(_UpperCAmelCase						)

					for t in self.progress_bar(self.scheduler.timesteps						):
							if guidance_scale > 1:
									snake_case__	     =latent_model_input[: len(_UpperCAmelCase						) // 2]
									snake_case__	     =torch.cat([half, half]					,   dim=0						)
							snake_case__	     =self.scheduler.scale_model_input(_UpperCAmelCase					,   _UpperCAmelCase						)

							snake_case__	     =t
							if not torch.is_tensor(_UpperCAmelCase						):
									# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
									# This would be a good case for the `match` statement (Python 3.10+)
									snake_case__	     =latent_model_input.device.type == 'mps'
									if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						):
											snake_case__	     =torch.floataa if is_mps else torch.floataa
									else:
											snake_case__	     =torch.intaa if is_mps else torch.intaa
									snake_case__	     =torch.tensor([timesteps]					,   dtype=_UpperCAmelCase					,   device=latent_model_input.device						)
							elif len(timesteps.shape						) == 0:
									snake_case__	     =timesteps[None].to(latent_model_input.device						)
							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							snake_case__	     =timesteps.expand(latent_model_input.shape[0]						)
							# predict noise model_output
							snake_case__	     =self.transformer(
							    _UpperCAmelCase					,   timestep=_UpperCAmelCase					,   class_labels=_UpperCAmelCase						).sample

							# perform guidance
							if guidance_scale > 1:
									snake_case__     ,	snake_case__	     =noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
									snake_case__     ,	snake_case__	     =torch.split(_UpperCAmelCase					,   len(_UpperCAmelCase						) // 2					,   dim=0						)

									snake_case__	     =uncond_eps + guidance_scale * (cond_eps - uncond_eps)
									snake_case__	     =torch.cat([half_eps, half_eps]					,   dim=0						)

									snake_case__	     =torch.cat([eps, rest]					,   dim=1						)

							# learned sigma
							if self.transformer.config.out_channels // 2 == latent_channels:
									snake_case__     ,	snake_case__	     =torch.split(_UpperCAmelCase					,   _UpperCAmelCase					,   dim=1						)
							else:
									snake_case__	     =noise_pred

							# compute previous image: x_t -> x_t-1
							snake_case__	     =self.scheduler.step(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						).prev_sample

					if guidance_scale > 1:
							snake_case__     ,	snake_case__	     =latent_model_input.chunk(2					,   dim=0						)
					else:
							snake_case__	     =latent_model_input

					snake_case__	     =1 / self.vae.config.scaling_factor * latents
					snake_case__	     =self.vae.decode(_UpperCAmelCase						).sample

					snake_case__	     =(samples / 2 + 0.5).clamp(0					,   1						)

					# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
					snake_case__	     =samples.cpu().permute(0					,   2					,   3					,   1						).float().numpy()

					if output_type == "pil":
							snake_case__	     =self.numpy_to_pil(_UpperCAmelCase						)

					if not return_dict:
							return (samples,)

					return ImagePipelineOutput(images=_UpperCAmelCase						)









'''simple docstring'''

from __future__ import annotations

SCREAMING_SNAKE_CASE__     :						List[Any]	=  1.6021e-19  # units = C






def 				a  (     UpperCamelCase_    :    float      ,					UpperCamelCase_    :    float      ,					UpperCamelCase_    :    float      ,					)       ->	tuple[str, float]:
		if (conductivity, electron_conc, mobility).count(0			) != 1:
				raise ValueError('You cannot supply more or less than 2 values'			)
		elif conductivity < 0:
				raise ValueError('Conductivity cannot be negative'			)
		elif electron_conc < 0:
				raise ValueError('Electron concentration cannot be negative'			)
		elif mobility < 0:
				raise ValueError('mobility cannot be negative'			)
		elif conductivity == 0:
				return (
				    "conductivity",
				    mobility * electron_conc * ELECTRON_CHARGE,
				)
		elif electron_conc == 0:
				return (
				    "electron_conc",
				    conductivity / (mobility * ELECTRON_CHARGE),
				)
		else:
				return (
				    "mobility",
				    conductivity / (electron_conc * ELECTRON_CHARGE),
				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




